Cortical impoverishment in a stable subgroup of schizophrenia: Validation across various stages of psychosis

The variable results of positive-negative research with schizophrenics underscore the importance of well-characterized, standardized measurement techniques. We report on the development and initial standardization of the Positive and Negative Syndrome Scale (PANSS) for typological and dimensional assessment. Based on two established psychiatric rating systems, the 30-item PANSS was conceived as an operationalized, drug-sensitive instrument that provides balanced representation of positive and negative symptoms and gauges their relationship to one another and to global psychopathology. It thus constitutes four scales measuring positive and negative syndromes, their differential, and general severity of illness. Study of 101 schizophrenics found the four scales to be normally distributed and supported their reliability and stability. Positive and negative scores were inversely correlated once their common association with general psychopathology was extracted, suggesting that they represent mutually exclusive constructs. Review of five studies involving the PANSS provided evidence of its criterion-related validity with antecedent, genealogical, and concurrent measures, its predictive validity, its drug sensitivity, and its utility for both typological and dimensional assessment.

The lack of an accepted standard for measuring cognitive change in schizophrenia has been a major obstacle to regulatory approval of cognition-enhancing treatments. A primary mandate of the National Institute of Mental Health's Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative was to develop a consensus cognitive battery for clinical trials of cognition-enhancing treatments for schizophrenia through a broadly based scientific evaluation of measures. The MATRICS Neurocognition Committee evaluated more than 90 tests in seven cognitive domains to identify the 36 most promising measures. A separate expert panel evaluated the degree to which each test met specific selection criteria. Twenty tests were selected as a beta battery. The beta battery was administered to 176 individuals with schizophrenia and readministered to 167 of them 4 weeks later so that the 20 tests could be compared directly. The expert panel ratings are presented for the initially selected 36 tests. For the beta battery tests, data on test-retest reliability, practice effects, relationships to functional status, practicality, and tolerability are presented. Based on these data, 10 tests were selected to represent seven cognitive domains in the MATRICS Consensus Cognitive Battery. The structured consensus method was a feasible and fair mechanism for choosing candidate tests, and direct comparison of beta battery tests in a common sample allowed selection of a final consensus battery. The MATRICS Consensus Cognitive Battery is expected to be the standard tool for assessing cognitive change in clinical trials of cognition-enhancing drugs for schizophrenia. It may also aid evaluation of cognitive remediation strategies.

Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria. This parcellation is available in the FreeSurfer package. First, a computer-assisted hand parcellation classified each vertex as sulcal or gyral, and these were then subparcellated into 74 labels per hemisphere. Twelve datasets were used to develop rules and algorithms (reported here) that produced labels consistent with anatomical rules as well as automated computational parcellation. The final parcellation was used to build an atlas for automatically labeling the whole cerebral cortex. This atlas was used to label an additional 12 datasets, which were found to have good concordance with manual labels. This paper presents a precisely defined method for automatically labeling the cortical surface in standard terminology. Copyright 2010 Elsevier Inc. All rights reserved.